ArticlePloS one2020
Estimation of time-varying reproduction numbers underlying epidemiological processes: A new statistical tool for the COVID-19 pandemic.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Gauging the Impact of Artificial Intelligence and Mathematical Modeling in Response to the COVID-19 Pandemic: A Systematic Review.BioMed research international · 2022Pooled it
- Estimating transmission parameters from stochastic epidemic models using survival analysis techniques.Statistical methods in medical research · 2026Article
- Model-based clustering of time-dependent observations with common structural changes.Statistics and computing · 2026Article
- Simulation-based inference of the time-dependent reproduction number from temporally aggregated and under-reported disease incidence time series data.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Time-varying reproduction number estimation: fusing compartmental models with generalized additive models.Journal of the Royal Society, Interface · 2025Article
- Effective Reproduction Number of Smear-Positive Pulmonary Tuberculosis in Iran: A Registry-Based Study (2011-2021).Journal of research in health sciences · 2024Article
- Health information management systems and practices in conflict-affected settings: the case of northwest Syria.Globalization and health · 2024Article
- Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models.PLoS computational biology · 2024Article
- Landscape-Scale Epidemiological Dynamics of SARS-CoV-2 in White-Tailed Deer.Transboundary and emerging diseases · 2024Article
- Estimating time-dependent contact: a multi-strain epidemiological model of SARS-CoV-2 on the island of Ireland.Global epidemiology · 2023Article
- Bayesian modeling of dynamic behavioral change during an epidemic.Infectious Disease Modelling · 2023Article
- Impact of vaccination on the entire population and dose-response relation of COVID-19.Vacunas · 2023Article
- Modeling the time-dependent transmission rate using gaussian pulses for analyzing the COVID-19 outbreaks in the world.Scientific reports · 2023Article
- An SEIR Model with Time-Varying Coefficients for Analyzing the SARS-CoV-2 Epidemic.Risk analysis : an official publication of the Society for Risk Analysis · 2023Article
- A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA.International journal of data science and analytics · 2023Article
- Mathematical modeling to study the impact of immigration on the dynamics of the COVID-19 pandemic: A case study for Venezuela.Spatial and spatio-temporal epidemiology · 2022Article
- Modeling the effects of the governmental responses to COVID-19 on transit demand: The case of Athens, Greece.Case studies on transport policy · 2022Article
- Generalized SIR (GSIR) epidemic model: An improved framework for the predictive monitoring of COVID-19 pandemic.ISA transactions · 2022Article
- Time-discrete SIR model for COVID-19 in Fiji.Epidemiology and infection · 2022Article
- Modeling COVID-19 Incidence by the Renewal Equation after Removal of Administrative Bias and Noise.Biology · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The coronavirus pandemic has rapidly evolved into an unprecedented crisis. The susceptible-infectious-removed (SIR) model and its variants have been used for modeling the pandemic. However, time-independent parameters in the classical models may not capture the dynamic transmission and removal processes, governed by virus containment strategies taken at various phases of the epidemic. Moreover, few models account for possible inaccuracies of the reported cases. We propose a Poisson model with time-dependent transmission and removal rates to account for possible random errors in reporting and estimate a time-dependent disease reproduction number, which may reflect the effectiveness of virus control strategies. We apply our method to study the pandemic in several severely impacted countries, and analyze and forecast the evolving spread of the coronavirus. We have developed an interactive web application to facilitate readers' use of our method.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.